A 5G network slicing system based on a smart grid

Abstract

The application discloses a 5G network slice system based on a smart power grid, relates to the network slice technical field, and is used for acquiring all target objects through a target acquisition unit, acquiring target data of the target objects by using the target acquisition unit, obtaining all target objects and corresponding analysis sections Di and resource core values Hi, and then performing habit analysis on the target objects, the corresponding analysis sections Di and the resource core values Hi by means of a habit analysis unit, determining each target object according to the distribution of the resource core values Hi of the analysis section Di of each target object, and the distribution values of each target object in different analysis sections; network resources can be flexibly distributed according to different conditions of each analysis section, and the real-time network resources can be monitored, and when the fluctuation is too large, the remaining network resources are immediately dispatched to provide support; the application is simple, effective and easy to use.

Description

Technical Field

[0001] This invention belongs to the field of network slicing technology, specifically a 5G network slicing system based on smart grids. Background Technology

[0002] Patent CN112888069A discloses a 5G network slicing system for urban center environments. Its features include a 5G communication module, a data storage module, a weight calculation module, a network slicing module, and a resource scheduling module. This system calculates the weights of various communication services and wireless network resources for 5G mobile phone users in urban center environments according to their set processing methods, and performs slice scheduling processing that conforms to service priorities. It acquires real-time 5G mobile communication base station layout data and network link data, slices physical network resources, and obtains the communication service priority ranking as the resource scheduling result. The resource scheduler uses this result to significantly reduce base station congestion and handover frequency, while maintaining a high level of user connections and total bandwidth usage over a long period. Base station coverage, single-slice bandwidth usage, and single-slice user usage remain stable and without significant fluctuations, greatly improving the overall utilization rate of 5G base station wireless resources.

[0003] However, for network resource slicing, there is no way to make temporary adjustments based on real-time conditions; therefore, a technical solution is proposed. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art;

[0005] To achieve the above objectives, according to an embodiment of the first aspect of the present invention, a 5G network slicing system based on a smart grid is proposed, comprising:

[0006] The system includes a target acquisition unit, a behavior analysis unit, a behavior record library, a self-slicing unit, and an execution unit.

[0007] Among them, the target acquisition unit is used to acquire all target objects, and the target data of the target objects are acquired by the target acquisition unit to obtain all target objects and their corresponding analysis segments Di and resource core values ​​Hi;

[0008] The target acquisition unit is used to transmit all target objects and their corresponding analysis segments Di and resource core values ​​Hi to the behavior analysis unit; the behavior analysis unit is used to perform behavior analysis on the target objects and their corresponding analysis segments Di and resource core values ​​Hi, and determine the allocation value of each target object in different analysis segments based on the distribution of resource core values ​​Hi in the analysis segment Di of each target object.

[0009] The behavior analysis unit is used to transmit the allocation values ​​of the target object in different analysis segments to the behavior record library, which is used to store the allocation values ​​of all target objects in different analysis segments in real time.

[0010] The self-slicing unit is used to perform network slicing by combining the habit record library and the execution unit, specifically in the following way:

[0011] Based on the real-time time, the current analysis segment is obtained. Based on the allocation value of each target object in the analysis segment, network resources are allocated and executed using the execution unit.

[0012] Compared with the prior art, the beneficial effects of the present invention are:

[0013] This invention uses a target acquisition unit to acquire all target objects, obtains the target data of the target objects, and obtains all target objects and their corresponding analysis segments Di and resource core values ​​Hi; then, it uses a behavior analysis unit to perform behavior analysis on the target objects and their corresponding analysis segments Di and resource core values ​​Hi, and determines the allocation value of each target object in different analysis segments based on the distribution of resource core values ​​Hi in the analysis segments Di of each target object;

[0014] It can flexibly allocate network resources according to the different situations of each analysis segment, and can also monitor network resources in real time. When the fluctuation is too large, it can immediately schedule other network resources to provide support. This invention is simple, effective and easy to use. Attached Figure Description

[0015] Figure 1 This is a structural block diagram of the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 This application provides a 5G network slicing system based on a smart grid.

[0018] As an embodiment of the present invention, it specifically includes:

[0019] The system includes a target acquisition unit, a behavior analysis unit, a behavior record library, a self-slicing unit, and an execution unit.

[0020] The target acquisition unit is used to acquire all target objects, which are all the corresponding 5G network services, including real-time session services, streaming services, interactive services, background services, and best-effort services. In specific applications, these are generally represented as voice transmission, live video streams, web pages, file transfer protocols, SMS services, and email, etc.

[0021] The target data of the target object is obtained using the target acquisition unit. The specific acquisition method is as follows:

[0022] Step 1: First, divide the day into 24 time periods. Of course, the specific number of time periods depends on the specific situation. The division of time periods starts from 0:00. Each hour is divided into an analysis segment and labeled as Di, i = 1, ..., 24.

[0023] Step 2: Then select any target object and obtain the resource occupancy value of each analysis segment. The resource occupancy value is the proportion of the total network resources occupied by the corresponding target object, and mark it as the resource occupancy value.

[0024] The resource usage for a single time period is collected once every T1 time interval during that time period. After the collection is completed, the average value is automatically calculated to obtain the represented value. T1 is a value preset by the administrator.

[0025] Step 3: Let i = 1, select the corresponding analysis segment D1, and obtain the resource occupancy of the analysis segment for X1 consecutive days, where X1 is a preset value;

[0026] Then, obtain all the resource occupancy values ​​for this analysis segment and label them as Yj, j = 1, ..., X1, representing the resource occupancy value for X1 days; obtain the mean value of Yj and label it as P;

[0027] The eccentricity value W of Yj is calculated using the following formula:

[0028]

[0029] When W exceeds X2, the mean value P at this time is marked as the resource core value; otherwise, the number of values ​​in Yj that exceed P is obtained and marked as the upper number, and the number of values ​​in Yj that are less than P is marked as the lower number; X2 is a preset value.

[0030] When the upper number exceeds the lower number, mark the maximum value in Yj and the average value of P as the resource core value;

[0031] Otherwise, mark the minimum value in Yj and the mean value of P as the resource core value;

[0032] Obtain the resource core value for the corresponding analysis segment;

[0033] Step 4: Increment the value of i by one, and continue to select all Di. Following the principle of Step 3, obtain the resource core values ​​of all analysis segments and label them as Hi, i = 1, ..., 24; and Hi and Di have a one-to-one correspondence.

[0034] Step 5: Obtain all analysis segments Di and their corresponding resource kernel values ​​Hi;

[0035] Step 6: Perform the same processing on all other target objects to obtain all target objects and their corresponding analysis segments Di and resource core values ​​Hi;

[0036] The target acquisition unit is used to transmit all target objects and their corresponding analysis segments Di and resource core values ​​Hi to the behavior analysis unit; the behavior analysis unit is used to perform behavior analysis on the target objects and their corresponding analysis segments Di and resource core values ​​Hi. The specific behavior analysis method is as follows:

[0037] S1: Select any target object and obtain the resource core value H i for all analysis segments Di;

[0038] S2: Then automatically calculate the mean of the resource core value Hi, mark it as the core mean, and calculate the eccentricity value based on the core mean. When the eccentricity value is less than or equal to X3, mark the value corresponding to each resource core value Hi as the allocation value of the corresponding analysis segment, and obtain the allocation value of the target object in different analysis segments.

[0039] S3: When the eccentricity value exceeds X3, the analysis segments Di are automatically sorted in descending order of the resource core value Hi. The analysis segments Hi corresponding to values ​​exceeding 1.3 times the core average value are marked with a high-frequency mark, and the average value of Hi values ​​of the analysis segments marked with high-frequency marks is marked as the corresponding allocation value.

[0040] The analysis segments corresponding to Hi values ​​that are less than 0.7 times the kernel mean are marked with a low-frequency label, and the mean of the Hi values ​​of the analysis segments marked with a low-frequency label is marked as the corresponding allocation value.

[0041] The remaining analysis segments are marked with intermediate frequency (IF) labels, and the mean Hi value corresponding to the analysis segments marked with IF labels is marked as the corresponding allocation value.

[0042] Obtain the assignment values ​​for all analysis segments;

[0043] S4: Process all the remaining target objects using steps S1-S3 to obtain the allocation value of each target object in different analysis segments;

[0044] The behavior analysis unit is used to transmit the allocation values ​​of the target object in different analysis segments to the behavior record library, which is used to store the allocation values ​​of all target objects in different analysis segments in real time.

[0045] The self-slicing unit is used to perform network slicing by combining the habit record library and the execution unit, specifically in the following way:

[0046] Based on the real-time time, the current analysis segment is obtained, network resources are allocated according to the allocation value of each target object in the analysis segment, and the execution unit is used to execute.

[0047] As a second embodiment of the present invention, a resource synchronization unit is also included. The resource synchronization unit is used to monitor the real-time network resources used by all target objects, mark them as practical values, and transmit them to the self-slicing unit. The self-slicing unit is used to perform real-time sampling analysis on the practical values. The specific method of real-time sampling analysis is as follows:

[0048] Once the target object with the corresponding practical value is obtained, it is marked as an overclocking object. Then, the current analysis segment and the allocation value corresponding to the overclocking object in that analysis segment are obtained.

[0049] When the actual value exceeds twice the allocated value, the allocated values ​​of all objects in the current analysis segment are automatically obtained and sorted in ascending order of the allocated values.

[0050] The allocation value of the target object is doubled, and in real time, according to the sorting method of the target objects, the minimum allocation value is reserved for the selected target object from the beginning to the end, and all the excess allocation value is given to the overclocking object. The minimum allocation value is the administrator's preset value.

[0051] The modified information is transmitted to the execution unit, which then executes the allocation according to the corresponding modified values.

[0052] As a third embodiment of the present invention, a management unit is also included, which is communicatively connected to the self-slicing unit and is used to input all preset values.

[0053] The data in the above formula are all calculated by removing the dimensions and taking the numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0054] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A 5G network slicing system based on a smart grid, characterized in that, include: The system includes a target acquisition unit, a behavior analysis unit, a behavior record library, a self-slicing unit, and an execution unit. The target acquisition unit is used to acquire all target objects, obtain the target data of the target objects, and obtain all target objects and their corresponding analysis segments Di and resource core values ​​Hi. The resource core value is determined based on the average and eccentric values ​​of the resource occupancy of analysis segments Di. The average and eccentric values ​​of the resource occupancy of analysis segments Di are determined based on the resource occupancy of analysis segments Di over X1 consecutive days. The resource occupancy value is used to indicate the proportion of the total network resources occupied by the target object. The method for acquiring all target objects and their corresponding analysis segments Di and resource core values ​​Hi is as follows: Step 1: First, divide the day into 24 time periods, starting from midnight. Each hour is divided into an analysis segment and labeled as Di, i = 1, ..., 24. Step 2: Then select any target object and obtain the resource occupancy value of each analysis segment. The resource occupancy value is the proportion of the total network resources occupied by the corresponding target object, and mark it as the resource occupancy value. The resource occupancy value for a single time period is collected once every T1 time interval within that time period. After collection, the average value is automatically calculated to obtain the represented value. T1 is a value preset by the administrator. Step 3: Let i = 1, select the corresponding analysis segment D1, and obtain the resource occupancy of the analysis segment for X1 consecutive days, where X1 is a preset value; Then, obtain all the resource occupancy values ​​for this analysis segment and label them as Yj, j = 1, ..., X1, representing the resource occupancy value for X1 days; obtain the mean value of Yj and label it as P; The eccentricity value W of Yj is calculated using the following formula: When W exceeds X2, the mean value P at this time is marked as the resource core value; otherwise, the number of values ​​in Yj that exceed P is obtained and marked as the upper number, and the number of values ​​in Yj that are less than P is marked as the lower number; X2 is a preset value. When the upper number exceeds the lower number, mark the maximum value in Yj and the mean value of P as the resource core value; otherwise, mark the minimum value in Yj and the mean value of P as the resource core value. Obtain the resource core value for the corresponding analysis segment; Step 4: Increment the value of i by one, continue to select all Di, and obtain the resource core values ​​of all analysis segments according to the principle of Step 3. Mark them as Hi, i = 1, ..., 24; and Hi and Di have a one-to-one correspondence. Step 5: Obtain all analysis segments Di and their corresponding resource kernel values ​​Hi; Step 6: Perform the same processing on all other target objects to obtain all target objects and their corresponding analysis segments Di and resource core values ​​Hi; The target acquisition unit is used to transmit all target objects and their corresponding analysis segments Di and resource core values ​​Hi to the behavior analysis unit; the behavior analysis unit is used to perform behavior analysis on the target objects and their corresponding analysis segments Di and resource core values ​​Hi, and determine the allocation value of each target object in different analysis segments based on the distribution of resource core values ​​Hi in the analysis segment Di of each target object. The behavior analysis unit is used to transmit the allocation values ​​of the target object in different analysis segments to the behavior record library, which is used to store the allocation values ​​of all target objects in different analysis segments in real time. The self-slicing unit is used to perform network slicing by combining the habit record library and the execution unit, specifically in the following way: Based on the real-time time, the current analysis segment is obtained. Based on the allocation value of each target object in the analysis segment, network resources are allocated and executed using the execution unit.

2. The 5G network slicing system based on a smart grid according to claim 1, characterized in that, The target objects are all the corresponding 5G network services, including real-time session services, streaming services, interactive services, background services, and best-effort services.

3. A 5G network slicing system based on a smart grid according to claim 1, characterized in that, The specific methods for habit analysis are as follows: S1: Select any target object and obtain the resource core value Hi of all analysis segments Di; S2: Then automatically calculate the mean of the resource core value Hi, mark it as the core mean, and calculate the eccentricity value based on the core mean. When the eccentricity value is less than or equal to X3, mark the value corresponding to each resource core value Hi as the allocation value of the corresponding analysis segment, and obtain the allocation value of the target object in different analysis segments. S3: When the eccentricity value exceeds X3, the analysis segments Di are automatically sorted in descending order of the resource core value Hi. The analysis segments with Hi values ​​exceeding 1.3 times the core average value are marked with a high-frequency mark, and the average Hi value of the analysis segments marked with the high-frequency mark is marked as the corresponding allocation value. The analysis segments corresponding to Hi values ​​that are 0.7 times lower than the kernel mean are marked with a low-frequency label, and the mean of Hi values ​​in the analysis segments marked with a low-frequency label is marked as the corresponding allocation value. The remaining analysis segments are marked with intermediate frequency (IF) labels, and the mean Hi value corresponding to the analysis segments marked with IF labels is marked as the corresponding allocation value. Obtain the assignment values ​​for all analysis segments; S4: Process all the remaining target objects using steps S1-S3 to obtain the allocation value of each target object in different analysis segments.

4. A 5G network slicing system based on a smart grid according to claim 1, characterized in that, It also includes a resource synchronization unit, which monitors the real-time network resources used by all target objects, marks them as usable values, and transmits them to the self-slicing unit. The self-slicing unit performs real-time sampling analysis on the usable values. The specific method of real-time sampling analysis is as follows: Once the target object with the corresponding practical value is obtained, it is marked as an overclocking object. Then, the current analysis segment and the allocation value corresponding to the overclocking object in that analysis segment are obtained. When the actual value exceeds twice the allocated value, the allocated values ​​of all objects in the current analysis segment are automatically obtained and sorted in ascending order of the allocated values. The allocation value of the target object is doubled, and in real time, according to the sorting method of the target objects, the minimum allocation value is reserved for the selected target object from the beginning to the end, and all the excess allocation value is given to the overclocking object. The minimum allocation value is the administrator's preset value. The modified information is transmitted to the execution unit, which then executes the allocation according to the corresponding modified values.

5. A 5G network slicing system based on a smart grid according to claim 1, characterized in that, It also includes a management unit, which communicates with the self-slicing unit and is used to input all preset values.

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